TradeMAV众筹RAMDeck多设备推理集线器
TradeMAV正在Indiegogo众筹售价109美元的RAMDeck，这是一款低功耗集线器，可协调Windows电脑、Mac、Linux设备和Android手机共同进行模型推理。它按层分配模型而非在硬件层面合并内存，底层采用免费的llama.cpp RPC，并增加设备发现、层调度、故障恢复、控制面板和OpenAI兼容API。厂商演示显示，混合专家模型速度较快，但稠密模型约为每秒两个token，集线器的处理器、内存、接口和功耗仍未披露。
RAMDeck把免费分布式推理机制封装成众筹便利产品，但规格缺失和厂商测试显示的速度权衡，划清了它与llama.cpp、exo DIY方案的差异。
- TradeMAV's RAMDeck hub is crowdfunding on Indiegogo, listed at $109 for early backers
- The engine underneath is llama.cpp's RPC backend, which anyone can already run at no cost
- Its fastest demos lean on an RTX 3060 and mixture-of-experts models, while denser models manage around two tokens per second
With DDR5 now costing roughly five times what it did in mid-2025, a small company called TradeMAV thinks the answer to running bigger AI models at home isn't buying more RAM, but borrowing the memory already sitting in your other devices.
The argument, which is hard to rule against, is being pitched in the form of RAMDeck, which TradeMAV is crowdfunding on Indiegogo, a small, low-power hub that links Windows PCs, Macs, Linux machines and Android phones on a home network into one memory pool, so a model too large for any single device can be split across all of them.
The campaign uses fixed funding against a $10,000 goal and closes on November 8 2026, 2026, according to the company's launch video, while a cost comparison table in TradeMAV's own RAM pooling guide lists the hub at $109 as an early-bird reward.